1. Eliminating Background-Bias for Robust Person Re-identification
Maoqing Tian, Shuai Yi, Hongsheng Li, Shihua Li,
Xuesen Zhang, Jianping Shi, Junjie Yan, Xiaogang Wang
CVPR 2018 Poster
2019/3/11
Xu Gao, Peking University.

2. Xu Gao, Peking University. gaoxu1024@pku.edu.cn
Background
Existing models are biased to capture too much relevance between background appearances.
已有方法忽视了background-bias，并且数据集也没有涵盖全，拍摄的camera比较少。
Probe Image
Same Person
Same Camera
Diff Person
Same Camera
Similar Background
Same Person
Diff Camera
2019/3/11
Xu Gao, Peking University.

3. Investigation on Background-Bias
Build four types of datasets from existing reid datasets.
Network framework: Learning Deep Feature Representations with Domain Guided Dropout for Person Re-Identification. CVPR 2016.
Foreground Mask: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. ArXiv 2016.
2019/3/11
Xu Gao, Peking University.

4. Investigation on Background-Bias
Existing model fails due to background bias.
Train on the original dataset.
Test on CUHK03.
Reason: Overfitting the training dataset with background bias.
2019/3/11
Xu Gao, Peking University.

5. Investigation on Background-Bias
Background can help distinguish persons?
Train on the background-only dataset.
Test on CUHK03.
The background appearances of the same persons are usually similar in existing datasets, and the networks are easily biased by such similar backgrounds.
2019/3/11
Xu Gao, Peking University.

6. Investigation on Background-Bias
How to eliminate background-bias?
Train on the mean-background and the random-background datasets.
Test on CUHK03.
Conclusion: Trained model from random-background data should be much more robust to new scenes.
2019/3/11
Xu Gao, Peking University.

7. Xu Gao, Peking University. gaoxu1024@pku.edu.cn
More Results
Test on CUHK03
Test on Market1501
在此处键入公式。
2019/3/11
Xu Gao, Peking University.

8. The Proposed Framework
Similar structure as the whole-person main network, followed by a upsampling module.
Output: 4-class parsing map (background + 3 foregrounds)
2019/3/11
Xu Gao, Peking University.

9. The Proposed Framework
Some results of the person parsing network.
2019/3/11
Xu Gao, Peking University.

10. Xu Gao, Peking University. gaoxu1024@pku.edu.cn
Training Scheme
Stage 1: Pretrain the person parsing network with LIP, Human-Parsing and MS-COCO human parsing datasets.
Stage 2: The whole-person main network is train independently.
Stage 3: Fix parameters of the main network, and train the person- region guided pooling sub-network.
Stage 4: The main network and the guided-pooling sub-network are trained end-to-end.
2019/3/11
Xu Gao, Peking University.

11. Xu Gao, Peking University. gaoxu1024@pku.edu.cn
Experiments
Training on HumanParsing, LIP, MS-COCO.
Testing on CUHK03, CUHK01, VIPER, 3dpes, Market-1501.
Top-K metric.
2019/3/11
Xu Gao, Peking University.

12. Xu Gao, Peking University. gaoxu1024@pku.edu.cn
Experiments
2019/3/11
Xu Gao, Peking University.

13. Evaluation on Background Influence Dataset
Compare with DGD.
DGD suffers great performance drop when testing on the mean- background and the random- background dataset.
2019/3/11
Xu Gao, Peking University.

14. Analysis of Each Component
To Do: More branches …
2019/3/11
Xu Gao, Peking University.

15. Xu Gao, Peking University. gaoxu1024@pku.edu.cn
Conclusion
+Investigation on background-bias.
+New ReID network with segmenting person into three parts.
-More experiments could be conducted.
-The framework is similar with previous works in attention.
2019/3/11
Xu Gao, Peking University.